4,853 research outputs found
From Data Topology to a Modular Classifier
This article describes an approach to designing a distributed and modular
neural classifier. This approach introduces a new hierarchical clustering that
enables one to determine reliable regions in the representation space by
exploiting supervised information. A multilayer perceptron is then associated
with each of these detected clusters and charged with recognizing elements of
the associated cluster while rejecting all others. The obtained global
classifier is comprised of a set of cooperating neural networks and completed
by a K-nearest neighbor classifier charged with treating elements rejected by
all the neural networks. Experimental results for the handwritten digit
recognition problem and comparison with neural and statistical nonmodular
classifiers are given
Multiple object tracking using a neural cost function
This paper presents a new approach to the tracking of multiple objects in CCTV surveillance using a combination of simple neural cost functions based on Self-Organizing Maps, and a greedy assignment algorithm. Using a reference standard data set and an exhaustive search algorithm for benchmarking, we show that the cost function plays the most significant role in realizing high levels of performance. The neural cost function’s context-sensitive treatment of appearance, change of appearance and trajectory yield better tracking than a simple, explicitly designed cost function. The algorithm matches 98.8% of objects to within 15 pixels
Review of Face Detection Systems Based Artificial Neural Networks Algorithms
Face detection is one of the most relevant applications of image processing
and biometric systems. Artificial neural networks (ANN) have been used in the
field of image processing and pattern recognition. There is lack of literature
surveys which give overview about the studies and researches related to the
using of ANN in face detection. Therefore, this research includes a general
review of face detection studies and systems which based on different ANN
approaches and algorithms. The strengths and limitations of these literature
studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
Communications and control for electric power systems: Power flow classification for static security assessment
This report investigates the classification of power system states using an artificial neural network model, Kohonen's self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen's self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed in this report, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed
Mapping the Galaxy Color-Redshift Relation: Optimal Photometric Redshift Calibration Strategies for Cosmology Surveys
Calibrating the photometric redshifts of >10^9 galaxies for upcoming weak
lensing cosmology experiments is a major challenge for the astrophysics
community. The path to obtaining the required spectroscopic redshifts for
training and calibration is daunting, given the anticipated depths of the
surveys and the difficulty in obtaining secure redshifts for some faint galaxy
populations. Here we present an analysis of the problem based on the
self-organizing map, a method of mapping the distribution of data in a
high-dimensional space and projecting it onto a lower-dimensional
representation. We apply this method to existing photometric data from the
COSMOS survey selected to approximate the anticipated Euclid weak lensing
sample, enabling us to robustly map the empirical distribution of galaxies in
the multidimensional color space defined by the expected Euclid filters.
Mapping this multicolor distribution lets us determine where - in galaxy color
space - redshifts from current spectroscopic surveys exist and where they are
systematically missing. Crucially, the method lets us determine whether a
spectroscopic training sample is representative of the full photometric space
occupied by the galaxies in a survey. We explore optimal sampling techniques
and estimate the additional spectroscopy needed to map out the color-redshift
relation, finding that sampling the galaxy distribution in color space in a
systematic way can efficiently meet the calibration requirements. While the
analysis presented here focuses on the Euclid survey, similar analysis can be
applied to other surveys facing the same calibration challenge, such as DES,
LSST, and WFIRST.Comment: ApJ accepted, 17 pages, 10 figure
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